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Fuzzy Inference System Tuning

Tune membership functions and rules of fuzzy systems

You can tune the membership function parameters and rules of your fuzzy inference system using Global Optimization Toolbox tuning methods such as genetic algorithms and particle swarm optimization. For more information, see Tuning Fuzzy Inference Systems.

If your system is a single-output type-1 Sugeno FIS, you can tune its membership function parameters using neuro-adaptive learning methods. This tuning method does not require Global Optimization Toolbox software. For more information, see Neuro-Adaptive Learning and ANFIS.


Fuzzy Logic DesignerDesign, test, and tune fuzzy inference systems


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tunefisTune fuzzy inference system or tree of fuzzy inference systems (Since R2019a)
tunefisOptionsOption set for tunefis function (Since R2019a)
getTunableSettingsObtain tunable settings from fuzzy inference system (Since R2019a)
setTunableSet specified parameter settings as tunable or nontunable (Since R2019a)
getTunableValuesObtain values of tunable parameters from fuzzy inference system (Since R2019a)
setTunableValuesSpecify tunable parameter values of a fuzzy inference system (Since R2019a)
anfisTune Sugeno-type fuzzy inference system using training data
anfisOptionsOption set for anfis function


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RuleSettingsTunable parameter settings of fuzzy rules (Since R2019a)
VariableSettingsTunable parameter settings of fuzzy variables (Since R2019a)
MembershipFunctionSettingsTunable parameter settings for fuzzy membership functions (Since R2019a)
MembershipFunctionSettingsType2Tunable parameter settings for type-2 fuzzy membership functions (Since R2019b)
ClauseParametersParameter settings for rule clauses (Since R2019a)
NumericParametersTunable numeric parameter settings of membership functions (Since R2019a)


Tune Fuzzy Systems

Train ANFIS Systems

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